



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class      _UpperCAmelCase      (							_SCREAMING_SNAKE_CASE  ):
     UpperCamelCase__		     =					['''image_processor''', '''tokenizer''']
     UpperCamelCase__		     =					'''LayoutLMv2ImageProcessor'''
     UpperCamelCase__		     =					('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')




     def __init__(					self			,    a__=None			,    a__=None			,    **a__):
         if "feature_extractor" in kwargs:
             warnings.warn(
                 '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                 ''' instead.'''			,    __UpperCAmelCase			,    )
             A__					=       kwargs.pop('''feature_extractor''')

         A__					=       image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
             raise ValueError('''You need to specify an `image_processor`.''')
         if tokenizer is None:
             raise ValueError('''You need to specify a `tokenizer`.''')

         super().__init__(__UpperCAmelCase			,    __UpperCAmelCase)




     def __call__(					self			,    a__			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = True			,    a__ = False			,    a__ = None			,    a__ = None			,    a__ = 0			,    a__ = None			,    a__ = None			,    a__ = None			,    a__ = False			,    a__ = False			,    a__ = False			,    a__ = False			,    a__ = True			,    a__ = None			,    **a__			,    ):
         # verify input
         if self.image_processor.apply_ocr and (boxes is not None):
             raise ValueError(
                 '''You cannot provide bounding boxes '''
                 '''if you initialized the image processor with apply_ocr set to True.''')

         if self.image_processor.apply_ocr and (word_labels is not None):
             raise ValueError(
                 '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.''')

         if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.''')

         # first, apply the image processor
         A__					=       self.image_processor(images=__UpperCAmelCase			,    return_tensors=__UpperCAmelCase)

         # second, apply the tokenizer
         if text is not None and self.image_processor.apply_ocr and text_pair is None:
             if isinstance(__UpperCAmelCase			,    __UpperCAmelCase):
                 A__					=       [text]  # add batch dimension (as the image processor always adds a batch dimension)
             A__					=       features['''words''']

         A__					=       self.tokenizer(
             text=text if text is not None else features['''words''']			,    text_pair=text_pair if text_pair is not None else None			,    boxes=boxes if boxes is not None else features['''boxes''']			,    word_labels=__UpperCAmelCase			,    add_special_tokens=__UpperCAmelCase			,    padding=__UpperCAmelCase			,    truncation=__UpperCAmelCase			,    max_length=__UpperCAmelCase			,    stride=__UpperCAmelCase			,    pad_to_multiple_of=__UpperCAmelCase			,    return_token_type_ids=__UpperCAmelCase			,    return_attention_mask=__UpperCAmelCase			,    return_overflowing_tokens=__UpperCAmelCase			,    return_special_tokens_mask=__UpperCAmelCase			,    return_offsets_mapping=__UpperCAmelCase			,    return_length=__UpperCAmelCase			,    verbose=__UpperCAmelCase			,    return_tensors=__UpperCAmelCase			,    **__UpperCAmelCase			,    )

         # add pixel values
         A__					=       features.pop('''pixel_values''')
         if return_overflowing_tokens is True:
             A__					=       self.get_overflowing_images(__UpperCAmelCase			,    encoded_inputs['''overflow_to_sample_mapping'''])
         A__					=       images

         return encoded_inputs




     def 		snake_case_		(					self			,    a__			,    a__):
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         A__					=       []
         for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx])

         if len(__UpperCAmelCase) != len(__UpperCAmelCase):
             raise ValueError(
                 '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
                 F" {len(__UpperCAmelCase)} and {len(__UpperCAmelCase)}")

         return images_with_overflow




     def 		snake_case_		(					self			,    *a__			,    **a__):
         return self.tokenizer.batch_decode(*__UpperCAmelCase			,    **__UpperCAmelCase)




     def 		snake_case_		(					self			,    *a__			,    **a__):
         return self.tokenizer.decode(*__UpperCAmelCase			,    **__UpperCAmelCase)




     @property
     def 		snake_case_		(					self):
         return ["input_ids", "bbox", "attention_mask", "image"]




     @property
     def 		snake_case_		(					self):
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,    __UpperCAmelCase			,    )
         return self.image_processor_class




     @property
     def 		snake_case_		(					self):
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,    __UpperCAmelCase			,    )
         return self.image_processor



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''pixel_values''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1 / 2_55    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Any      		=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														A      :					Any      		=					get_size_dict(__UpperCAmelCase						)
														A      :					List[Any]      		=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Dict      		=					do_resize
														A      :					Tuple      		=					size
														A      :					Union[str, Any]      		=					resample
														A      :					Dict      		=					do_center_crop
														A      :					int      		=					crop_size
														A      :					Union[str, Any]      		=					do_rescale
														A      :					str      		=					rescale_factor
														A      :					Optional[Any]      		=					do_normalize
														A      :					Dict      		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														A      :					Dict      		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					int      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return resize(
														    __UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						resample=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					Optional[Any]      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return center_crop(__UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														return rescale(__UpperCAmelCase    ,						scale=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       PIL.Image.Image:
														A      :					Optional[int]      		=					do_resize if do_resize is not None else self.do_resize
														A      :					Optional[Any]      		=					resample if resample is not None else self.resample
														A      :					Tuple      		=					do_center_crop if do_center_crop is not None else self.do_center_crop
														A      :					Optional[Any]      		=					do_rescale if do_rescale is not None else self.do_rescale
														A      :					str      		=					rescale_factor if rescale_factor is not None else self.rescale_factor
														A      :					Tuple      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Optional[int]      		=					image_mean if image_mean is not None else self.image_mean
														A      :					Dict      		=					image_std if image_std is not None else self.image_std

														A      :					List[Any]      		=					size if size is not None else self.size
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase						)
														A      :					Dict      		=					crop_size if crop_size is not None else self.crop_size
														A      :					Tuple      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Any      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

														# All transformations expect numpy arrays.
														A      :					List[Any]      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if do_resize:
																					A      :					List[str]      		=					[self.resize(image=__UpperCAmelCase    ,						size=__UpperCAmelCase    ,						resample=__UpperCAmelCase						) for image in images]

														if do_center_crop:
																					A      :					Dict      		=					[self.center_crop(image=__UpperCAmelCase    ,						size=__UpperCAmelCase						) for image in images]

														if do_rescale:
																					A      :					Union[str, Any]      		=					[self.rescale(image=__UpperCAmelCase    ,						scale=__UpperCAmelCase						) for image in images]

														if do_normalize:
																					A      :					Tuple      		=					[self.normalize(image=__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase						) for image in images]

														A      :					Optional[Any]      		=					[to_channel_dimension_format(__UpperCAmelCase    ,						__UpperCAmelCase						) for image in images]

														A      :					List[Any]      		=					{'''pixel_values''': images}
														return BatchFeature(data=__UpperCAmelCase    ,						tensor_type=__UpperCAmelCase						)






